Tag Archives: demography

Life expectancy update, disparity edition

The good news is that U.S. life expectancy is at a record high, 78.8 as of 2012.

What about life disparity — the inequality in life expectancy? With the economic crisis and rise in income inequality, it would be great to know. However, the National Center for Health Statistics hasn’t released detailed life tables with data more recent than 2008, so I can’t yet update the data for the analysis I did last year, so here it is reposted instead:

Life Expectancy, Life Disparity

Reposted from July 23, 2013

In 2008 the life expectancy at birth in the U.S. was 78.1. That means that if a group children born in 2008 lived every year of their lives exposed to the risks of death observed in 2008, their average lifespan would be 78.1 years. But those who made it to age 60 would live an average of 22.7 more years, for a total of 82.7. And those who live to age 99 would live an average of 2.4 more years, for an average of 101.4.

So “life expectancy” as commonly used is not a prediction of how long today’s babies will live — since we hope the future is better than living 2008 over and over — and it’s not a prediction of how long your elderly loved ones will live.

Life disparity

Life expectancy — for any age — is a measure of central tendency: the average number of years of life remaining. And so there is a dispersion around that mean. That dispersion is inequality. A very nice article in the open-access journal BMJ Open, by James Vaupel, Zhen Zhang and Alyson A van Raalte, describes the measure of life disparity. It’s complicated, but a neat tool.

Life disparity is the average number of years people are expected to live when they die. For example, in the U.S. in 2008 an infant who died on the first day of life died 78.1 years early. And a 78-year-old who died, counterintuitively, died 10 years early (since the life expectancy at 78 is 10). To understand what this measure means, consider that if everyone died at exactly 78.1 years of age, life expectancy would be unchanged but life disparity would be 0. On the other hand, the greatest life disparity would occur if all early occurred at age 0.

Life disparity and life expectancy usually go together. That’s because reducing early deaths has the biggest effect on both measures. Here is the cool figure from that paper:

The association between life disparity in a specific year and life expectancy in that year for males in 40 countries and regions, 1840–2009. The black triangle represents the USA in 2007; the USA had a male life expectancy 3.78 years lower than the international record in 2007 and a life disparity 2.8 years greater. The brown points denote years after 1950, the orange points 1900–1949 and the yellow points 1840–1900. The light blue triangles represent countries with the lowest life disparity but with a life expectancy below the international record in the specific year; the dark blue triangles indicate the life expectancy leaders in a given year, with life disparities greater than the most egalitarian country in that year. The black point at (0,0) marks countries with the lowest life disparity and the highest life expectancy. During the 170 years from 1840 to 2009, 89 holders of record life expectancy also enjoyed the lowest life disparity.

The association between life disparity in a specific year and life expectancy in that year for males in 40 countries and regions, 1840–2009. The black triangle represents the USA in 2007; the USA had a male life expectancy 3.78 years lower than the international record in 2007 and a life disparity 2.8 years greater. The brown points denote years after 1950, the orange points 1900–1949 and the yellow points 1840–1900. The light blue triangles represent countries with the lowest life disparity but with a life expectancy below the international record in the specific year; the dark blue triangles indicate the life expectancy leaders in a given year, with life disparities greater than the most egalitarian country in that year. The black point at (0,0) marks countries with the lowest life disparity and the highest life expectancy. During the 170 years from 1840 to 2009, 89 holders of record life expectancy also enjoyed the lowest life disparity.

Countries at the bottom left (0,0) have both the world’s highest life expectancy and the lowest life disparity in the world for that year, which occurred 89 times over 170 years. Countries below the diagonal have relatively low life disparity given their life expectancy; those above the diagonal (like the U.S.) have higher-than-expected life disparity for their level of life expectancy. In our case that reflects the fact that we do a pretty good job keeping old people alive, but let too many young people die.

U.S. improvement

The good news is that life expectancy is increasing in the U.S. (and most other places), and that the inequality between Blacks and Whites is getting smaller, as reported by the National Center for Health Statistics. That is, the Black-White inequality in average expectation of life at birth has shrunk.

The mixed news is that life disparity is much higher for Blacks than Whites — but that gap is falling as well. Here are those numbers for 1998 and 2008 (I did the life disparity calculations from this and this, and will happily share the spreadsheet). Click to enlarge:

expectancydisparity

So Black deaths are more dispersed than White deaths: 14 and 13 for males and females, compared with 12 and 11. For comparison, the Swedish female life disparity is 9. What does a higher disparity mean? Generally, a larger share of early deaths. That’s why the race gap in life expectancy at birth is greater than the race gap in life expectancy at older ages — average 65-year-old Whites and Blacks have more similar life expectancies than do infants.

Why is life disparity more interesting than life expectancy alone, and how does this help explain Black-White inequality in the U.S.? For one thing, high life disparity indicates either relatively unhealthy or dangerous living conditions at younger ages. So it’s partly a measure of the quality of life. Vaupel et al. add:

Reducing early-life disparities helps people plan their less-uncertain lifetimes. A higher likelihood of surviving to old age makes savings more worthwhile, raises the value of individual and public investments in education and training, and increases the prevalence of long-term relationships. Hence, healthy longevity is a prime driver of a country’s wealth and well-being. While some degree of income inequality might create incentives to work harder, premature deaths bring little benefit and impose major costs. Moreover, equity in the capability to maintain good health is central to any larger concept of societal justice.

I think what they say about differences between countries would apply to differences between groups within a society as well.

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This word ‘generation,’ I do not think it means what you think it means

The people who make up these things drive me bananas.

NPR launched a new series on “millennials” yesterday, called “New Boom,” with this dramatic declaration: “There are more millennials in America right now than baby boomers — more than 80 million of us.”

The definition NPR gives for this generation is “people born between 1980 and 2000.” And it’s true there are more than 80 million of them. In fact, there are 91 million of them, according to the 2012 American Community Survey data you can get from IPUMS.org.* That’s OK, though, because there are only 76 million Baby Boomers, so the claim checks out.

But what’s a generation?

The Baby Boom was a demographic event. In 1946, after the end of World War II, the crude birth rate — the number of births per 1,000 population — jumped from 20.4 to 24.1, the biggest one-year change recorded in U.S. history. The birth rate didn’t fall back to its previous level until 1965. That’s why the Baby Boom went down in history as 1946 to 1964. Because that’s when it happened.

This figure shows the number of living people by birth year, and the crude birth rate recorded in each year, using the NPR definition of millennials (in red), compared with the baby boom (purple):

mellenials.xlsx

Even with population growth I reckon the people born in the years 1946-1964 might outnumber the self-promoting millennials if not for the weight of mortality pulling down the purple bars. But if the young NPR reporters want to brag about outnumbering a generation that is starting to lose its older members to old age (and who are, after all, their parents), then I guess the shoe fits.

The Baby Boom was not a generation. It was a cohort, “a group of people sharing a common demographic experience” (in this case birth during the same period). That demographic event happens to have lasted 18 years, which is unfortunate because that may have contributed to the tendency to declare “generations” of similar lengths.

The Pew Research people, who do lots of interesting work on social change that uses generational concepts, use these slightly different definitions for four generations: Silent Generation, born 1928-1945; the Baby Boom Generation, born 1946-1964; Generation X, born 1965-1980; Millennial Generation, born 1981 and later (Pew says “no chronological endpoint has been set for this group,” which is awkward because if they’re really still going, the oldest are 33 and they have children that are the same generation as themselves**). Ironic, isn’t it, that Pew constructs “Generation X” as the shortest of the four (some generation, a mere 16 years!) before declaring them “America’s neglected ‘middle child.’

Real generations rarely have starting and ending points on a population level. Populations usually just keeping having births every year in smooth patterns of increase or decrease without discrete edges, so generations overlap. Even in families it gets hard to nail down generations once you start moving horizontally; siblings born many years apart are in the same generation, but the cousins get all confused.

Meaningful cohorts, on the other hand, can be defined all over the place, such as: the people who graduated college during the Great Recession, people who introduced the Internet to their parents, and so on. These are not generations.

In 2010, when crisis was really in the air, I was on the NPR show The State of Things in North Carolina, discussing the Baby Boom (no audio online). After attempting to clarify the difference between a generation and a cohort, I offered this dramatic example of a cohort — people born in 1960 specifically:

So if you were born in 1960, graduated college in 1982, and entered the labor force in the middle of an awful recession, then managed to pull some kind of career together, got married and divorced, by the 90s it was time to be downsized already for the first time, you’re 40 in 2000, and it’s time for the dot-com bubble, you’re out of your job again, and here you are ready for your retirement, finally, you’ve been left in your own 401(k), having to put together your own pension, and of course now that’s in the tank and your house isn’t worth anything. So that insecurity and instability is really imprinted this group. We talk about the 60s, and civil rights and antiwar, and great music and everything, but that’s seeming like a long time ago now for people who are looking at retirement.

I don’t know if anyone actually had that experience, but it seems likely.

Anyway, if people really want to keep using these generation labels, and it seems unlikely to stop now given the marketing payoff from naming rights, than that’s the way it goes. But please don’t ask demographers to define them.

Notes

* This is a little different from the population estimates the Census Bureau produces, which are coded by age rather than year of birth. I use the ACS data because they report year of birth, and because it’s easier. The differences are very small.

** Thanks to Mo Willow for pointing this out.

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Diversity is the new normal

I have new briefing paper out today with the Council on Contemporary Families, titled “Family Diversity is the New Normal for America’s Children.” I’ll post news links soon. In the meantime:

I’m happy to provide high quality graphics.

Let me know what you think!

Reports and commentary:

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Immigrant health paradox update

I wrote a few years ago about the surprisingly low infant mortality rates among immigrants, especially Mexican immigrants, given their relative socioeconomic status. As poor as they other, in other words, we would expect higher infant mortality rates than they have. This has been called the epidemiological paradox. Here is an update, which includes some text from the previous post.

In almost every race/ethnic group, immigrants are healthier.* Here’s the pattern for infant mortality, now updated with 2010 infant mortality rates from federal vital statistics records (click to enlarge).

epipara

For Latinos in particular, their health is surprisingly good given their economic conditions. Robert Hummer and colleagues, in a 2007 article, offered a succinct description:

…the relatively low levels of education, income, and health insurance coverage among Hispanics compared with non-Hispanic whites is thought to place the former at higher risk for negative health outcomes. However, it is well documented that some Hispanic groups exhibit similar observed death rates compared with the non-Hispanic white population and much lower death rates than the non-Hispanic black population, whom they closely resemble with respect to socioeconomic characteristics. The greatest enigma is exhibited by the Mexican-origin population of the United States. This Hispanic subgroup is characterized by low educational attainment; low health insurance coverage rates; mortality rates similar to non-Hispanic whites; and much more favorable mortality rates than those of non-Hispanic blacks across most of the life course.

In a 2013 revisiting of the paradox, Daniel Powers confirms the basic pattern, but adds an important wrinkle for Mexican mothers: the foreign-born advantage disappears for older mothers. Thus, children born to older Mexican immigrants have similar risks as those who mothers are born in the U.S. He concludes, in part:

Given the association between infant survival and maternal health, differential infant survival within the Mexican-origin population suggests that longer exposure to social conditions in the U.S. undermines the health of mothers who, in general, seem to have more favorable health endowments than their non-Hispanic white counterparts as evidenced by the relatively lower rates of infant mortality at younger ages.

Immigrants are often healthier than the average people in the countries they came from, which explains some of the paradox. However, our ability to accurately assess the relative health of immigrants versus the populations they left behind is limited by available data. Further, in the case of Mexico, the situation is complicated by cyclical movements of immigration and emigration. In a recent paper, Georgiana Bostean reviews this problem, and compares the health of immigrants, non-migrants, and return migrants to Mexico. And — It’s complicated. She concludes:

…there is no simple explanation for Latinos’ perplexing health outcomes, such as simply that healthier people migrate. Rather, migrants are positively selected in some health aspects, negatively selected in others, and in yet other health outcomes, there is no selection effect. In sum, selective migration plays a role in explaining some of U.S. Latinos’ health outcomes, but is not the only explanation and does not account for the Paradox.

These articles are a good place to start on this topic: lots of references to fill in the background and previous research on this paradox, which goes back at least to the 1980s. This is a fascinating and important research area, dealing with such questions as health behaviorintergenerational change, thorny puzzles about different immigrant groups, child development and lots more.

*Because Puerto Rico is part of the U.S. (albeit not a free part), people born in Puerto Rico who move to the states are not immigrants, just migrants. In the figure I used the terms “US Born” and “Foreign born,” but this is just shorthand, and not strictly accurate for Puerto Ricans.

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Marriage, divorce, remarriage, age, education (Coontz tabs edition)

Stephanie Coontz has an excellent Op-Ed on the front of today’s New York Times Sunday Review, which draws out the implications for family instability of the connection between increasing gender equality on the one hand, and increasing economic inequality and insecurity on the other. The new instability is disproportionately concentrated among the population with less than a college degree. To help with her research, I gave Stephanie the figure below, but it didn’t make the final cut. This shows the marriage history of men and women by education and age. She wrote:

According to the sociologist Philip N. Cohen, among 40-somethings with at least a bachelor’s degree, as of 2012, 63 percent of men and 59 percent of women were in their first marriage, compared to just 43 percent of men and 42 percent of women without a bachelor’s degree.

I highlighted those numbers in the figure. Also striking is the higher percentage of divorced people among those with less than a BA degree (and higher widowhood rates). Click to enlarge: age marriage history Cross-posted on the Families As They Really Are blog.

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Border fences make unequal neighbors

 

israelgazafence

 

There is one similarity between the Israel/Gaza crisis and the U.S. unaccompanied child immigrant crisis: National borders enforcing social inequality. When unequal populations are separated, the disparity creates social pressure at the border. The stronger the pressure, the greater the military force needed to maintain the separation.

To get a conservative estimate of the pressure at the Israel/Gaza border, I compared some numbers for Israel versus Gaza and the West Bank combined, from the World Bank (here’s a recent rundown of living conditions in Gaza specifically). I call that conservative because things are worse in Gaza than in the West Bank.

Then, just as demographic wishful thinking, I calculated what the single-state solution would look like on the day you opened the borders between Israel, the West Bank, and Gaza. I added country percentiles showing how each state ranks on the world scale (click to enlarge).

israelwbgaza

Israel’s per capita income is 6.2-times greater, its life expectancy is 6 years longer, its fertility rate is a quarter lower, and its age structure is reversed. Together, the Palestinian territories have a little more than half the Israeli population (living on less than 30% of the land). That means that combining them all into one country would move both populations’ averages a lot. For example, the new country would be substantially poorer (29% poorer) and younger than Israel, while increasing the national income of Palestinians by 444%. Israelis would fall from the 17th percentile worldwide in income, and the Palestinians would rise from the 69th, to meet at the 25th percentile.

Clearly, the separation keeps poor people away from rich people. Whether it increases or decreases conflict is a matter of debate.

Meanwhile

Meanwhile, the USA has its own enforced exclusion of poor people.

Photo of US/Tijuana border by Kordian from Flickr Creative Commons

Photo of US/Tijuana border by Kordian from Flickr Creative Commons.

The current crisis at the southern border of the USA mostly involves children from Guatemala, Honduras, and El Salvador. They don’t actually share a border with the USA, of course, but their region does, and crossing into Mexico seems pretty easy, so it’s the same idea.

To make a parallel comparison to Israel and the West Bank/Gaza, I just used Guatemala, which is larger by population than Honduras and El Salvador combined, and also closest to the USA. The economic gap between the USA and Guatemala is even larger than the Israeli/Palestinian gap. However, because the USA is 21-times larger than Guatemala by population, we could easily absorb the entire Guatemalan population without much damaging our national averages. Per capita income in the USA, for example, would fall only 4%, while rising more than 7-times for Guatemala (click to enlarge):

guatemalausa

This simplistic analysis yields a straightforward hypothesis: violence and military force at national borders rises as the income disparity across the border increases. Maybe someone has already tested that.

The demographic solution is obvious: open the borders, release the pressure, and devote resources to improving quality of life and social harmony instead of enforcing inequality. You’re welcome!

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What a recovery looks like (with population growth by age)

If you don’t account for population growth, I don’t get what you’re saying with these employment numbers. I’ll show a simple example, but first a little rundown on Friday’s jobs report.

Here is how CNN Money played the jobs report:

cnn-jobs

What does it mean, this loss and gain of jobs, returning finally to where we started? Four paragraphs under that happy headline, CNN did points out:

Given population growth over the last four years, the economy still needs more jobs to truly return to a healthy place. How many more? A whopping 7 million, calculates Heidi Shierholz, an economist with the Economic Policy Institute.

So what does “Finally!” mean? The Wall Street Journal ran the headline, “Jobs Return to Peak, but Quality Lags.” On 538 it was, “Women returned to prerecession levels of employment in 2013. Men remain hundreds of thousands of jobs in the hole:”

538-jobs

The Center on Budget and Policy Priorities showed how much better the previous recoveries were:

cbpp-jobs

That’s a good comparison. And CBPP mentioned population growth, too:

…payroll employment has finally topped its level at the start of the recession. Still, with essentially no net job growth since December 2007 but a growing working-age population, many more people today want to work but don’t have a job.

It’s not that no one mentions population growth, it’s that they still lead with the “top line” number. And they all have that horizontal line at the raw number of jobs when the recession started as the benchmark. I don’t know why.

Maybe in some crazy economics world the absolute number of jobs is what really matters for evaluating a recovery, and that explains the fixation on that horizontal line. From a social perspective what matters is the proportion of people with jobs. I could see the logic if you had a finite number of employers that never change, where you could literally count up the jobs at two points in time, and see who added and who subtracted from their payrolls (this is why retail chains report “same-store” trends, so the statistics aren’t confounded by the changing number of stores). But we have zillions of employers, constantly changing and moving around — largely in response to population changes. So that static image seems pointless.

In perspective

So here are some charts to put the recession and recovery in slightly better perspective. These all use the Bureau of Labor Statistics’ Current Population Survey from March 2003 to March 2013 (from IPUMS), the household survey used to track the labor force. I use ages 15 and older, and combine people in school (up to age 24) with those employed (not how most people do it, but a lot of people went to school, or stayed in school, because of the bad job market, and it doesn’t make sense to count them as not simply not employed). The survey excludes people in institutions, like prisons, and on-base military personnel.

To show the basic issue, here are the changes in the non-institutionalized population, age 15+, along with the number of them employed or in school — showing absolute changes relative to 2008, the peak employment year.

popjobs1

The 15+ population increased almost 12 million from 2008 to 2013. People employed or in school was not yet back to 2008 levels in March 2013. So a basic population adjustment is the least you can ask for (and we get that from the BLS with the employment-population ratio, which as of May was up less than one percent in the last 3.5 years, but it’s not the headline number).

What about age shifts? You don’t expect extreme age composition changes in 5 years, but there are different employment trends at different ages, so those affect how many employed people we are short. Here are the trends in work/school, by age and sex:

popjobs2

This makes it look like the 30-49s that are getting crushed. The 50+ community’s gains, however,are deceptive — their population is increasing. In fact, the population of people 30-49 declined 5% during this decade, while the population 50+ increased almost 30%. The younger people have increased their schooling rates, but their population has also grown. If you look at the employment/school rates, you see that among men, it is the younger groups that have done worst:

popjobs3

Women clearly are doing better (partly because in the 20-29 range they’re going to school more). It is amazing that employment rates didn’t fall at all over age 60. This could be because the population increase in that group is all in Baby Boomers just hitting their sixties, but I reckon it’s also people delaying retirement compensating for unemployment.

Now that we have age-specific work/school rates, and population changes, we can easily calculate how many people in each age group would have to be in work/school to get to the number implied by applying the peak-year 2008 rates to the population in each year. Sorry this one is so ugly: I made the last bar for each group pink to show the bottom line, where each group stands in 2013 relative to 2008:

popjobs4

Worst off are the 20-something men, down more than a million worker/students in 2013. Interestingly, women are only better off in the 20-something and 50+ ranges.

Finally, if you sum these figures you get the total, age-adjusted losses, by sex since 2008, as of March 2013:

popjobs5

And that’s your bottom line. The job/school losses stood at 3.3 million for men and 2.4 million for women as of March 2013.*

Really, there are no huge surprises here. In fact, the total population change is not a bad rough adjustment, especially for the short term. But there are some interesting nuances here. And with all the data and computers we have these days, let’s adjust for age and sex.

*I don’t say that’s how many “jobs” we need, because I don’t think “jobs” exist — are created, destroyed, shipped overseas, etc. I think there are employed people, people getting jobs, losing jobs, etc. I don’t see how a “job” exists absent a worker in it (and no, a listing is not a job until they fill it). So we don’t need to “create jobs” after a recession, what we need to do is “hire people.” Pet peeve.

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